import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam






class 		_lowerCamelCase		( datasets.BeamBasedBuilder     ):
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     return datasets.DatasetInfo(
         features=datasets.Features({'''content''': datasets.Value('''string''' )} )			,			supervised_keys=lowerCAmelCase			,			)
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase )							->       Dict:
     return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,			gen_kwargs={'''examples''': get_test_dummy_examples()} )]



    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase )							->       str:
     import apache_beam as beam

     return pipeline | "Load Examples" >> beam.Create(lowerCAmelCase )






class 		_lowerCamelCase		( datasets.BeamBasedBuilder     ):
    def     UpperCamelCase_					(	self )							->       List[str]:
     return datasets.DatasetInfo(
         features=datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string''' )} )} )			,			supervised_keys=lowerCAmelCase			,			)
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase )							->       List[str]:
     return [
         datasets.SplitGenerator(name=datasets.Split.TRAIN			,			gen_kwargs={'''examples''': get_test_nested_examples()} )
     ]



    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase )							->       List[str]:
     import apache_beam as beam

     return pipeline | "Load Examples" >> beam.Create(lowerCAmelCase )







def       A__   (						):
 return [(i, {"content": content}) for i, content in enumerate(['''foo''', '''bar''', '''foobar''']   )]







def       A__   (						):
 return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['''foo''', '''bar''', '''foobar''']   )]






class 		_lowerCamelCase		( UpperCamelCase_     ):
    @require_beam
    def     UpperCamelCase_					(	self )							->       str:
     SCREAMING_SNAKE_CASE__: Optional[int]=   len(get_test_dummy_examples() )
     with tempfile.TemporaryDirectory() as tmp_cache_dir:
      SCREAMING_SNAKE_CASE__: str=   DummyBeamDataset(cache_dir=lowerCAmelCase			,			beam_runner='''DirectRunner''' )
      builder.download_and_prepare()
      self.assertTrue(
          os.path.exists(
              os.path.join(lowerCAmelCase			,			builder.name			,			'''default'''			,			'''0.0.0'''			,			f'{builder.name}-train.arrow' ) ) )
      self.assertDictEqual(builder.info.features			,			datasets.Features({'''content''': datasets.Value('''string''' )} ) )
      SCREAMING_SNAKE_CASE__: str=   builder.as_dataset()
      self.assertEqual(dset['''train'''].num_rows			,			lowerCAmelCase )
      self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples			,			lowerCAmelCase )
      self.assertDictEqual(dset['''train'''][0]			,			get_test_dummy_examples()[0][1] )
      self.assertDictEqual(
          dset['''train'''][expected_num_examples - 1]			,			get_test_dummy_examples()[expected_num_examples - 1][1] )
      self.assertTrue(
          os.path.exists(os.path.join(lowerCAmelCase			,			builder.name			,			'''default'''			,			'''0.0.0'''			,			'''dataset_info.json''' ) ) )
      del dset
    @require_beam
    def     UpperCamelCase_					(	self )							->       int:
     import apache_beam as beam

     SCREAMING_SNAKE_CASE__: Tuple=   beam.io.parquetio.WriteToParquet

     SCREAMING_SNAKE_CASE__: List[Any]=   len(get_test_dummy_examples() )
     with tempfile.TemporaryDirectory() as tmp_cache_dir:
      SCREAMING_SNAKE_CASE__: str=   DummyBeamDataset(cache_dir=lowerCAmelCase			,			beam_runner='''DirectRunner''' )
      with patch('''apache_beam.io.parquetio.WriteToParquet''' ) as write_parquet_mock:
       SCREAMING_SNAKE_CASE__: Union[str, Any]=   partial(lowerCAmelCase			,			num_shards=2 )
       builder.download_and_prepare()
      self.assertTrue(
          os.path.exists(
              os.path.join(
                  lowerCAmelCase			,			builder.name			,			'''default'''			,			'''0.0.0'''			,			f'{builder.name}-train-00000-of-00002.arrow' ) ) )
      self.assertTrue(
          os.path.exists(
              os.path.join(
                  lowerCAmelCase			,			builder.name			,			'''default'''			,			'''0.0.0'''			,			f'{builder.name}-train-00000-of-00002.arrow' ) ) )
      self.assertDictEqual(builder.info.features			,			datasets.Features({'''content''': datasets.Value('''string''' )} ) )
      SCREAMING_SNAKE_CASE__: Optional[Any]=   builder.as_dataset()
      self.assertEqual(dset['''train'''].num_rows			,			lowerCAmelCase )
      self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples			,			lowerCAmelCase )
      # Order is not preserved when sharding, so we just check that all the elements are there
      self.assertListEqual(sorted(dset['''train''']['''content'''] )			,			sorted(['''foo''', '''bar''', '''foobar'''] ) )
      self.assertTrue(
          os.path.exists(os.path.join(lowerCAmelCase			,			builder.name			,			'''default'''			,			'''0.0.0'''			,			'''dataset_info.json''' ) ) )
      del dset
    @require_beam
    def     UpperCamelCase_					(	self )							->       List[str]:
     with tempfile.TemporaryDirectory() as tmp_cache_dir:
      SCREAMING_SNAKE_CASE__: List[str]=   DummyBeamDataset(cache_dir=lowerCAmelCase )
      self.assertRaises(datasets.builder.MissingBeamOptions			,			builder.download_and_prepare )



    @require_beam
    def     UpperCamelCase_					(	self )							->       List[str]:
     SCREAMING_SNAKE_CASE__: int=   len(get_test_nested_examples() )
     with tempfile.TemporaryDirectory() as tmp_cache_dir:
      SCREAMING_SNAKE_CASE__: Tuple=   NestedBeamDataset(cache_dir=lowerCAmelCase			,			beam_runner='''DirectRunner''' )
      builder.download_and_prepare()
      self.assertTrue(
          os.path.exists(
              os.path.join(lowerCAmelCase			,			builder.name			,			'''default'''			,			'''0.0.0'''			,			f'{builder.name}-train.arrow' ) ) )
      self.assertDictEqual(
          builder.info.features			,			datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string''' )} )} ) )
      SCREAMING_SNAKE_CASE__: List[str]=   builder.as_dataset()
      self.assertEqual(dset['''train'''].num_rows			,			lowerCAmelCase )
      self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples			,			lowerCAmelCase )
      self.assertDictEqual(dset['''train'''][0]			,			get_test_nested_examples()[0][1] )
      self.assertDictEqual(
          dset['''train'''][expected_num_examples - 1]			,			get_test_nested_examples()[expected_num_examples - 1][1] )
      self.assertTrue(
          os.path.exists(os.path.join(lowerCAmelCase			,			builder.name			,			'''default'''			,			'''0.0.0'''			,			'''dataset_info.json''' ) ) )
      del dset


"""simple docstring"""


def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   float:




						'''simple docstring'''



						return price * (1 + tax_rate)


if __name__ == "__main__":
	print(F'''{price_plus_tax(100, 0.25) = }''')
	print(F'''{price_plus_tax(125.50, 0.05) = }''')


